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Add superconductivity screening modes
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‎README.md‎

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[![CI](https://img.shields.io/github/actions/workflow/status/chatmaterials/superconductivity-analysis/ci.yml?branch=main&label=CI)](https://github.com/chatmaterials/superconductivity-analysis/actions/workflows/ci.yml) [![Release](https://img.shields.io/github/v/release/chatmaterials/superconductivity-analysis?display_name=tag)](https://github.com/chatmaterials/superconductivity-analysis/releases)
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Standalone skill for superconductivity-relevant DFT result analysis, including candidate ranking by Tc, phonon stability, and mu* robustness.
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Standalone skill for superconductivity-relevant DFT result analysis, including candidate ranking by Tc, phonon stability, mu* robustness, and application mode.
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## Install
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@@ -19,7 +19,9 @@ python3 scripts/analyze_alpha2f.py fixtures/alpha2f/alpha2f.dat --json
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python3 scripts/analyze_phonon_modes.py fixtures/phonon/phonon_modes.dat --json
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python3 scripts/estimate_tc.py --alpha2f-path fixtures/alpha2f/alpha2f.dat --mu-star 0.10 --json
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python3 scripts/analyze_tc_sensitivity.py --alpha2f-path fixtures/alpha2f/alpha2f.dat --json
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python3 scripts/compare_superconducting_candidates.py fixtures fixtures/candidates/stable-strong fixtures/candidates/unstable-strong --mu-star 0.10 --target-tc 0.5 --json
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python3 scripts/compare_superconducting_candidates.py fixtures fixtures/candidates/soft-high fixtures/candidates/stable-strong fixtures/candidates/unstable-strong --mu-star 0.10 --target-tc 0.5 --mode balanced --json
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python3 scripts/compare_superconducting_candidates.py fixtures fixtures/candidates/soft-high fixtures/candidates/stable-strong --mu-star 0.10 --target-tc 0.5 --mode high-tc --json
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python3 scripts/compare_superconducting_candidates.py fixtures fixtures/candidates/soft-high fixtures/candidates/stable-strong --mu-star 0.10 --target-tc 0.5 --mode robust --json
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python3 scripts/export_superconductivity_report.py --alpha2f-path fixtures/alpha2f/alpha2f.dat --phonon-path fixtures/phonon/phonon_modes.dat --mu-star 0.10
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python3 scripts/run_regression.py
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```

‎SKILL.md‎

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---
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name: "superconductivity-analysis"
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description: "Use when the task is to analyze superconductivity-relevant quantities from DFT or Eliashberg-style outputs, including electron-phonon coupling summaries, logarithmic phonon frequency estimates, phonon-stability checks, Allen-Dynes Tc estimates, mu*-sensitivity or Tc robustness checks, candidate ranking, and compact markdown reports from finished calculations."
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description: "Use when the task is to analyze superconductivity-relevant quantities from DFT or Eliashberg-style outputs, including electron-phonon coupling summaries, logarithmic phonon frequency estimates, phonon-stability checks, Allen-Dynes Tc estimates, mu*-sensitivity or Tc robustness checks, mode-specific candidate ranking, and compact markdown reports from finished calculations."
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---
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# Superconductivity Analysis
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- check whether a phonon mode set contains soft or imaginary modes
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- estimate a simple Allen-Dynes `Tc`
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- estimate how sensitive Tc is to the chosen `mu*`
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- rank multiple superconducting candidates with a compact Tc-plus-stability heuristic
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- rank multiple superconducting candidates in balanced, high-Tc, robust, or stable modes
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- write a compact superconductivity-analysis report from existing data
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## Use the bundled helpers

‎fixtures/candidates/soft-high/alpha2f.dat‎

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5.0 0.06
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30.0 0.09

‎fixtures/candidates/soft-high/phonon_modes.dat‎

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0.60
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2.20
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3.80
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5.60
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7.90

‎references/superconductivity.md‎

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- Soft modes can enhance coupling, but fully imaginary modes signal structural instability.
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- Candidate ranking is useful for triage, but stability penalties should be treated as hard constraints before claiming a promising superconducting material.
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- Tc sensitivity to `mu*` is a useful robustness check; a promising candidate should not collapse entirely under modest changes in Coulomb pseudopotential.
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- Application mode matters: a high-Tc screen may reasonably tolerate soft modes that a stability-first screen would reject.

‎scripts/compare_superconducting_candidates.py‎

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@@ -20,7 +20,7 @@ def locate_required(root: Path, relative_paths: list[str]) -> Path:
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raise SystemExit(f"Could not locate any of {relative_paths} in {root}")
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def analyze_case(root: Path, mu_star: float, target_tc: float) -> dict[str, object]:
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def analyze_case(root: Path, mu_star: float, target_tc: float, mode: str) -> dict[str, object]:
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alpha2f_path = locate_required(root, ["alpha2f.dat", "alpha2f/alpha2f.dat"])
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phonon_path = locate_required(root, ["phonon_modes.dat", "phonon/phonon_modes.dat"])
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epc = analyze_alpha2f(alpha2f_path)
@@ -36,7 +36,14 @@ def analyze_case(root: Path, mu_star: float, target_tc: float) -> dict[str, obje
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screening_tc *= 0.85
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elif phonons["stability_class"] == "unstable":
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screening_tc = 0.0
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score = tc_penalty + instability_penalty + softness_penalty + robustness_penalty
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if mode == "high-tc":
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score = -0.1 * screening_tc + 0.5 * instability_penalty + 0.1 * softness_penalty + 0.1 * robustness_penalty
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elif mode == "robust":
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score = 1.0 * tc_penalty + 1.5 * instability_penalty + 1.0 * softness_penalty + 1.5 * robustness_penalty
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elif mode == "stable":
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score = 0.5 * tc_penalty + 2.0 * instability_penalty + 1.5 * softness_penalty + 0.5 * robustness_penalty
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else:
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score = tc_penalty + instability_penalty + softness_penalty + robustness_penalty
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return {
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"case": root.name,
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"path": str(root),
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}
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def analyze_cases(roots: list[Path], mu_star: float, target_tc: float) -> dict[str, object]:
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cases = [analyze_case(root, mu_star, target_tc) for root in roots]
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def analyze_cases(roots: list[Path], mu_star: float, target_tc: float, mode: str) -> dict[str, object]:
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cases = [analyze_case(root, mu_star, target_tc, mode) for root in roots]
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ranked = sorted(cases, key=lambda item: item["screening_score"])
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return {
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"mu_star": mu_star,
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"target_tc_K": target_tc,
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"ranking_basis": "screening_score = tc_penalty + instability_penalty + softness_penalty + robustness_penalty",
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"mode": mode,
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"ranking_basis": "screening_score = weighted(tc_penalty, instability_penalty, softness_penalty, robustness_penalty)",
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"cases": ranked,
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"best_case": ranked[0]["case"] if ranked else None,
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"observations": [
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parser.add_argument("paths", nargs="+")
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parser.add_argument("--mu-star", type=float, default=0.10)
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parser.add_argument("--target-tc", type=float, default=1.0)
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parser.add_argument("--mode", choices=["balanced", "high-tc", "robust", "stable"], default="balanced")
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parser.add_argument("--json", action="store_true")
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args = parser.parse_args()
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payload = analyze_cases([Path(path).expanduser().resolve() for path in args.paths], args.mu_star, args.target_tc)
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payload = analyze_cases([Path(path).expanduser().resolve() for path in args.paths], args.mu_star, args.target_tc, args.mode)
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if args.json:
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print(json.dumps(payload, indent=2))
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return

‎scripts/run_regression.py‎

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ranked = run_json(
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"scripts/compare_superconducting_candidates.py",
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"fixtures",
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"fixtures/candidates/soft-high",
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"fixtures/candidates/stable-strong",
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"fixtures/candidates/unstable-strong",
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"--mu-star",
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"0.10",
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"--target-tc",
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"0.5",
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"--mode",
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"balanced",
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"--json",
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)
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ensure(ranked["best_case"] == "stable-strong", "superconductivity-analysis should rank the strong and stable candidate ahead of the weaker or unstable cases")
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high_tc_ranked = run_json(
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"scripts/compare_superconducting_candidates.py",
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"fixtures",
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"fixtures/candidates/soft-high",
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"fixtures/candidates/stable-strong",
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"--mu-star",
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"0.10",
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"--target-tc",
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"0.5",
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"--mode",
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"high-tc",
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"--json",
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)
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ensure(high_tc_ranked["best_case"] == "soft-high", "superconductivity-analysis should rank the higher-Tc softened candidate first in high-tc mode")
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robust_ranked = run_json(
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"scripts/compare_superconducting_candidates.py",
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"fixtures",
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"fixtures/candidates/soft-high",
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"fixtures/candidates/stable-strong",
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"--mu-star",
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"0.10",
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"--target-tc",
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"0.5",
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"--mode",
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"robust",
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"--json",
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)
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ensure(robust_ranked["best_case"] == "stable-strong", "superconductivity-analysis should rank the robust stable candidate first in robust mode")
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temp_dir = Path(tempfile.mkdtemp(prefix="superconductivity-analysis-report-"))
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try:
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report_path = Path(

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